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By analyzing the cellular functions of genetically imprinted genes as annotated in the Gene Ontology for human and mouse, we found that imprinted genes are often involved in developmental, transport and regulatory processes. In the human, paternally expressed genes are enriched in GO terms related to the development of organs and of anatomical structures. In the mouse, maternally expressed genes regulate cation transport as well as G-protein signaling processes. Furthermore, we investigated if imprinted genes are regulated by common transcription factors. We identified 25 TF families that showed an enrichment of binding sites in the set of imprinted genes in human and 40 TF families in mouse. In general, maternally and paternally expressed genes are not regulated by different transcription factors. The genes Nnat, Klf14, Blcap, Gnas and Ube3a contribute most to the enrichment of TF families. In the mouse, genes that are maternally expressed in placenta are enriched for AP1 binding sites. In the human, we found that these genes possessed binding sites for both, AP1 and SP1.

A crucial process in biological cells is the translocation of newly synthesized proteins across cell membranes via integral membrane protein pores termed translocons. Recent improved techniques now allow producing artificial membranes with pores of similar dimensions of a few nm as the translocon system. For the translocon system, the protein has to be unfolded, whereas the artificial pores are wide enough so that small proteins can pass through even when folded. To study how proteins permeate through such membrane pores, we used coarse-grained Brownian dynamics simulations where the proteins were modeled as single beads or bead-spring polymers for both folded and unfolded states. The pores were modeled as cylindrical holes through the membrane with various radii and lengths. Diffusion was driven by a concentration gradient created across the porous membrane. Our results for both folded and unfolded configurations show the expected reciprocal relation between the flow rate and the pore length in agreement with an analytical solution derived by Brunn et al. [Q. J. Mech. Appl. Math. 37, 311 (1984)]. Furthermore, we find that the geometric constriction by the narrow pore leads to an accumulation of proteins at the pore entrance, which in turn compensates for the reduced diffusivity of the proteins inside the pore.

It is well known that bulk water plays a crucial role in the biological assembly of hydrophilic surfaces. In this work, we emphasize the special molecular nature of bridging water networks in the formation of biomolecular contacts driven by electrostatic interactions. We have studied the assembly of two hydrophilic protein interfaces. Extensive atomistic molecular dynamics simulations reproducably recovered the native bound state of the Barnase:Barstar complex as seen in the crystal structure of the complex and thus give atomistic insight into the mechanism of binding. The simulations showed the structured water in the interfacial gap to play an adhesive role between the interfaces by forming a strong hydrogen bond network between the interfaces with a reduced dielectric constant compared to bulk. The role of this network is relevant already during the diffusive phase and stabilizes the early intermediate states before native contacts are formed. The convergence to the stereo-specific complex was accompanied by maximizing the interfacial water-mediation and formation of the highly hydrated stereo-specific complex. We introduce a new graph-based methodology to quantify the connectness of water networks.

We study the formation of protein-protein encounter complexes with a Langevin equation approach that considers direct, steric and thermal forces. As three model systems with distinctly different properties we consider the pairs barnase:barstar, cytochrome c:cytochrome c peroxidase and p53:MDM2. In each case, proteins are modeled either as spherical particles, as dipolar spheres or as collection of several small beads with one dipole. Spherical reaction patches are placed on the model proteins according to the known experimental structures of the protein complexes. In the computer simulations, concentration is varied by changing box size. Encounter is defined as overlap of the reaction patches and the corresponding first passage times are recorded together with the number of unsuccessful contacts before encounter. We find that encounter frequency scales linearly with protein concentration, thus proving that our microscopic model results in a well-defined macroscopic encounter rate. The number of unsuccessful contacts before encounter decreases with increasing encounter rate and ranges from 20-9000. For all three models, encounter rates are obtained within one order of magnitude of the experimentally measured association rates. Electrostatic steering enhances association up to 50-fold. If diffusional encounter is dominant (p53:MDM2) or similarly important as electrostatic steering (barnase:barstar), then encounter rate decreases with decreasing patch radius. More detailed modeling of protein shapes decreases encounter rates by 5-95 percent. Our study shows how generic principles of protein-protein association are modulated by molecular features of the systems under consideration. Moreover it allows us to assess different coarse-graining strategies for the future modelling of the dynamics of large protein complexes.

Ordered water molecules are observed by crystallography and nuclear magnetic resonance to mediate protein-ligand interactions. Here, we examine the energetics of hydrating cavities formed at protein-ligand interfaces using molecular dynamics simulations. The free energies of hydrating two cavities in the active site of two liganded complexes of cytochrome P450cam were calculated by multiconfigurational thermodynamic integration. The complex of cytochrome P450cam with 2-phenyl-imidazole contains a crystallographically well defined water molecule mediating hydrogen bonds between the protein and the inhibitor. We calculate that this water molecule is stabilized by a binding free energy of -11.6 +/- kJ/mol. The complex of cytochrome P450cam with its natural substrate, camphor, contains a cavity that is empty in the crystal structure although a water molecule in it could make a hydrogen bond to camphor. Here, solvation of this cavity is calculated to be unfavorable by +15.8 +/- 5.0 kJ/mol. The molecular dynamics simulations can thus distinguish a hydrated interfacial cavity from an empty one. They also provide support for the notion that protein-ligand complexes can accommodate empty interfacial cavities and that such cavities are likely to be unhydrated unless more than one hydrogen bond can be made to a water molecule in the cavity.

Background Identifying the gene regulatory networks governing the workings and identity of cells is one of the main challenges in understanding processes such as cellular differentiation, reprogramming or cancerogenesis. One particular challenge is to identify the main drivers and master regulatory genes that control such cell fate transitions. In this work, we reformulate this problem as the optimization problems of computing a Minimum Dominating Set and a Minimum Connected Dominating Set for directed graphs. Results Both MDS and MCDS are applied to the well-studied gene regulatory networks of the model organisms E. coli and S. cerevisiae and to a pluripotency network for mouse embryonic stem cells. The results show that MCDS can capture most of the known key player genes identified so far in the model organisms. Moreover, this method suggests an additional small set of transcription factors as novel key players for governing the cell-specific gene regulatory network which can also be investigated with regard to diseases. To this aim, we investigated the ability of MCDS to define key drivers in breast cancer. The method identified many known drug targets as members of the MDS and MCDS. Conclusions This paper proposes a new method to identify key player genes in gene regulatory networks. The Java implementation of the heuristic algorithm explained in this paper is available as a Cytoscape plugin at http://apps.cytoscape.org/apps/mcds. The SageMath programs for solving integer linear programming formulations used in the paper are available at https://github.com/maryamNazarieh/KeyRegulatoryGenesand as supplementary material. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0329-5) contains supplementary material, which is available to authorized users.

A 1 ns molecular dynamics simulation was performed to study the dynamic behavior of wild-type green fluorescent protein from Aequorea victoria. We find the protein to be remarkably rigid, both overall, because the cylindrical β-barrel provides a stable framework, but also on an atomic level in the immediate surrounding of the chromophore. Here, a tight H-bond network is formed mainly involving six internal water molecules. The perfect barrel is interrupted only between β-strands 7 and 8 where contact is made via side chain interactions, and we investigated the dynamic behavior of this region in detail. After ca. 320 ps of simulation, an arginine residue, initially sticking out into solution, folded over the cleft to form a H-bond with a backbone oxygen atom on the opposite strand. This contact appears important for stabilization of the overall protein architecture.

Background The Sec61 channel mediates protein translocation across the endoplasmic reticulum (ER) membrane during secretory protein biogenesis, and likely also during export of misfolded proteins for ER-associated degradation (ERAD). The mechanisms of channel opening for the different modes of translocation are not understood so far, but the position of the large ER-lumenal loop 7 of Sec61p suggests a decisive role. Results We show here that the Y345H mutation in L7 which causes diabetes in the mouse displays no ER import defects in yeast, but a delay in misfolded protein export. A complete deletion of L7 in Sec61p resulted in viable, cold- and tunicamycin-hypersensitive yeast cells with strong defects in posttranslational protein import of soluble proteins into the ER, and in ERAD of soluble substrates. Membrane protein ERAD was only moderately slower in sec61∆L7 than in wildtype cells. Although Sec61∆L7 channels were unstable in detergent, co-translational protein integration into the ER membrane, proteasome binding to Sec61∆L7 channels, and formation of hetero-heptameric Sec complexes were not affected. Conclusions We conclude that L7 of Sec61p is required for initiation of posttranslational soluble protein import into and misfolded soluble protein export from the ER, suggesting a key role for L7 in transverse gating of the Sec61 channel.

Background Breast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. In the light of the availability of tumor genomic and epigenomic data from different sources and experiments, new integrative approaches are needed to boost the probability of identifying such genetic key drivers. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets. Results Our results showed strong association between regulatory elements from different data sources in terms of the mutual regulatory influence and genomic proximity. By analyzing different types of regulatory interactions, TF-gene, miRNA-mRNA, and proximity analysis of somatic variants, we identified 106 genes, 68 miRNAs, and 9 mutations that are candidate drivers of oncogenic processes in breast cancer. Moreover, we unraveled regulatory interactions among these key drivers and the other elements in the breast cancer network. Intriguingly, about one third of the identified driver genes are targeted by known anti-cancer drugs and the majority of the identified key miRNAs are implicated in cancerogenesis of multiple organs. Also, the identified driver mutations likely cause damaging effects on protein functions. The constructed gene network and the identified key drivers were compared to well-established network-based methods. Conclusion The integrated molecular analysis enabled by the presented network-based approach substantially expands our knowledge base of prospective genomic drivers of genes, miRNAs, and mutations. For a good part of the identified key drivers there exists solid evidence for involvement in the development of breast carcinomas. Our approach also unraveled the complex regulatory interactions comprising the identified key drivers. These genomic drivers could be further investigated in the wet lab as potential candidates for new drug targets. This integrative approach can be applied in a similar fashion to other cancer types, complex diseases, or for studying cellular differentiation processes.

Modern simulation techniques are beginning to study the dynamic assembly and disassembly of multi-protein systems. In these many- particle simulations it can be very tedious to monitor the formation of specific structures such as fully assembled protein complexes or virus capsids above a background of monomers and partially assembled complexes. How- ever, such analyses can be performed conveniently when the spatial configuration is mapped onto a dynamically updated interaction graph [1]. On the example of Monte Carlo simulations of spherical particles with either isotropic or directed mutual attractions we demonstrate that this combined strategy allows for an efficient and also detailed analysis of complex formation in many-particle systems. Some more examples illustrate how time-dependent many-particle agglomeration can be visualized [2] or classified as either ordered or amorphous.[1] F. Lauck, V. Helms, T. Geyer, J. Chem. Theory Comput. 5 (2009) 641[2] T. Geyer, BMC Biophys. 4 (2011) 7